Karate Club

Benedek Rozemberczki, Oliver Kiss, Rik Sarkar
2020 Proceedings of the 29th ACM International Conference on Information & Knowledge Management  
Graphs encode important structural properties of complex systems. Machine learning on graphs has therefore emerged as an important technique in research and applications. We present Karate Club -a Python framework combining more than 30 state-of-the-art graph mining algorithms. These unsupervised techniques make it easy to identify and represent common graph features. The primary goal of the package is to make community detection, node and whole graph embedding available to a wide audience of
more » ... chine learning researchers and practitioners. Karate Club is designed with an emphasis on a consistent application interface, scalability, ease of use, sensible out of the box model behaviour, standardized dataset ingestion, and output generation. This paper discusses the design principles behind the framework with practical examples. We show Karate Club's efficiency in learning performance on a wide range of real world clustering problems and classification tasks along with supporting evidence of its competitive speed.
doi:10.1145/3340531.3412757 dblp:conf/cikm/RozemberczkiKS20 fatcat:bplqx2zgknfjtgbzuj3743ruum